ModRes: A modification of the ResPlan dataset ready for floor plan analysis and graph machine learning

Abstract:Floor plan datasets underpin computational analysis of building layouts, spatial connectivity and graph machine learning. However, publicly available datasets often require substantial preprocessing before they can be used reliably. We present ModRes, a cleaned and enriched version of the ResPlan dataset, containing 8,431 valid and unique residential floor plans. Invalid and duplicate floor plans are removed through a dedicated cleaning workflow, producing a ready-to-use dataset. For each floor plan, ModRes provides two-dimensional geometry, images, three-dimensional building representations and three complementary dual graphs in CSV and JSON formats with graph-, node- and edge-level features. Continuous and categorical graph attributes support machine learning workflows. We characterise the dataset using graph-based statistics and demonstrate its utility through a range of case studies. ModRes provides structured geometric, topological and semantic representations needed to automate spatial analysis and support machine learning-based workflows for building design and performance evaluation.

Machine Learning (cs.LG)

arXiv:2610.17178 [cs.LG]

(or arXiv:2610.17178v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2610.17178 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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